(1e) Managing Data Spaces, Performing MD, and Analyzing Trajectories with Signac, HOOMD-Blue, and Freud

Authors: 
Glotzer, S. C., University of Michigan
Adorf, C. S., University of Michigan
Ramasubramani, V., University of Michigan
Anderson, J. A., University of Michigan
We demonstrate how to efficiently develop a complete, integrated, and reproducible simulation workflow from the setup of a parameter space to the execution of simulations and the post-processing of output data for analysis and visualization, with Signac, HOOMD-blue, and Freud. Signac [1] provides a simple interface to map parameter spaces to simulation data, manage and search data and metadata, and develop scalable computational workflows. Signac-flow chains and executes data space operations and can easily submit workflows to high-performance clusters. HOOMD-blue [2] performs molecular dynamics and hard particle Monte Carlo simulations of particles fast on GPUs. Freud [3] analyzes particle simulation trajectories and computes order parameters, identifies clusters, and produces potential of mean force and torque plots. All these tools are Python libraries and work well together in a cohesive workflow, but each may also be used on its own. Signac provides a command line interface to ease integration of non-python tools in the workflow. The HOOMD-blue simulation engine may be used on its own, or with other workflow and analysis tools. Freud provides generic analysis techniques that can be applied to particle trajectory data from other simulation tools or experimental data. In this workshop, we will introduce the basics of each package and walk attendees through a complete integrated simulation workflow that combines their strengths.

[1] https://glotzerlab.engin.umich.edu/signac
[2] https://glotzerlab.engin.umich.edu/hoomd-blue/
[3] https://glotzerlab.engin.umich.edu/freud/